




'''simple docstring'''




import tempfile
import unittest

from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
    require_sentencepiece,
    require_tokenizers,
    require_torch,
    slow,
    torch_device,
)

from ...generation.test_utils import GenerationTesterMixin
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
     import torch

     from transformers import AutoTokenizer, UMTaForConditionalGeneration, UMTaForQuestionAnswering, UMTaModel


class     __UpperCamelCase			:






       def __init__(				self,						lowerCAmelCase,						lowerCAmelCase=99,						lowerCAmelCase=13,						lowerCAmelCase=7,						lowerCAmelCase=9,						lowerCAmelCase=True,						lowerCAmelCase=True,						lowerCAmelCase=False,						lowerCAmelCase=32,						lowerCAmelCase=5,						lowerCAmelCase=4,						lowerCAmelCase=37,						lowerCAmelCase=8,						lowerCAmelCase=0.1,						lowerCAmelCase=0.0_0_2,						lowerCAmelCase=1,						lowerCAmelCase=0,						lowerCAmelCase=0,						lowerCAmelCase=None,						lowerCAmelCase=None,						):



              """simple docstring"""







              lowerCamelCase_				=parent
              lowerCamelCase_				=batch_size
              lowerCamelCase_				=encoder_seq_length
              lowerCamelCase_				=decoder_seq_length
              # For common tests
              lowerCamelCase_				=self.decoder_seq_length
              lowerCamelCase_				=is_training
              lowerCamelCase_				=use_attention_mask
              lowerCamelCase_				=use_labels
              lowerCamelCase_				=vocab_size
              lowerCamelCase_				=hidden_size
              lowerCamelCase_				=num_hidden_layers
              lowerCamelCase_				=num_attention_heads
              lowerCamelCase_				=d_ff
              lowerCamelCase_				=relative_attention_num_buckets
              lowerCamelCase_				=dropout_rate
              lowerCamelCase_				=initializer_factor
              lowerCamelCase_				=eos_token_id
              lowerCamelCase_				=pad_token_id
              lowerCamelCase_				=decoder_start_token_id
              lowerCamelCase_				=None
              lowerCamelCase_				=decoder_layers






       def 	lowercase__	(				self    ):



              """simple docstring"""







              return TaConfig.from_pretrained('''google/umt5-base'''    )






       def 	lowercase__	(				self,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase=None,						lowerCAmelCase=None,						lowerCAmelCase=None,						lowerCAmelCase=None,						lowerCAmelCase=None,						):



              """simple docstring"""







              if attention_mask is None:
                     lowerCamelCase_				=input_ids.ne(config.pad_token_id    )
              if decoder_attention_mask is None:
                     lowerCamelCase_				=decoder_input_ids.ne(config.pad_token_id    )
              if head_mask is None:
                     lowerCamelCase_				=torch.ones(config.num_hidden_layers,						config.num_attention_heads,						device=lowerCAmelCase    )
              if decoder_head_mask is None:
                     lowerCamelCase_				=torch.ones(config.num_decoder_layers,						config.num_attention_heads,						device=lowerCAmelCase    )
              if cross_attn_head_mask is None:
                     lowerCamelCase_				=torch.ones(
                         config.num_decoder_layers,						config.num_attention_heads,						device=lowerCAmelCase    )
              return {
                  "input_ids": input_ids,
                  "decoder_input_ids": decoder_input_ids,
                  "attention_mask": attention_mask,
                  "decoder_attention_mask": decoder_attention_mask,
                  "head_mask": head_mask,
                  "decoder_head_mask": decoder_head_mask,
                  "cross_attn_head_mask": cross_attn_head_mask,
              }






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=ids_tensor([self.batch_size, self.encoder_seq_length],						self.vocab_size    )
              lowerCamelCase_				=ids_tensor([self.batch_size, self.decoder_seq_length],						self.vocab_size    )

              # we need to clamp the input ids here to avoid having pad token in between
              # this is because for NllbMoe the position_ids are prepared such that
              # all pad tokens have pos id = 2 and rest are between 2..seq_length
              # and the seq_length here is seq_length - num_pad_tokens
              # but when using past, there is no way of knowing if the past input ids had
              # pad tokens in them, which results in incorrect seq_lenth and which in turn results in
              # position_ids being off by num_pad_tokens in past input
              lowerCamelCase_				=input_ids.clamp(self.pad_token_id + 1    )
              lowerCamelCase_				=decoder_input_ids.clamp(self.pad_token_id + 1    )

              lowerCamelCase_				=self.get_config()
              lowerCamelCase_				=config.num_attention_heads
              lowerCamelCase_				=self.prepare_inputs_dict(lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase    )
              return config, input_dict






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_, lowerCamelCase_				=self.prepare_config_and_inputs()
              return config, inputs_dict






       def 	lowercase__	(				self    ):



              """simple docstring"""







              return TaConfig(
                  vocab_size=166,						d_model=self.hidden_size,						d_ff=self.d_ff,						d_kv=self.hidden_size // self.num_attention_heads,						num_layers=self.num_hidden_layers,						num_decoder_layers=self.decoder_layers,						num_heads=self.num_attention_heads,						relative_attention_num_buckets=self.relative_attention_num_buckets,						dropout_rate=self.dropout_rate,						initializer_factor=self.initializer_factor,						eos_token_id=self.eos_token_id,						bos_token_id=self.pad_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.decoder_start_token_id,						)






       def 	lowercase__	(				self    ):



              """simple docstring"""







              return TaConfig(
                  vocab_size=self.vocab_size,						d_model=self.hidden_size,						d_ff=self.d_ff,						d_kv=self.hidden_size // self.num_attention_heads,						num_layers=self.num_hidden_layers,						num_decoder_layers=self.decoder_layers,						num_heads=self.num_attention_heads,						relative_attention_num_buckets=self.relative_attention_num_buckets,						dropout_rate=self.dropout_rate,						initializer_factor=self.initializer_factor,						eos_token_id=self.eos_token_id,						bos_token_id=self.pad_token_id,						pad_token_id=self.pad_token_id,						decoder_start_token_id=self.decoder_start_token_id,						)






       def 	lowercase__	(				self,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						):



              """simple docstring"""







              lowerCamelCase_				=UMTaModel(config=lowerCAmelCase    )
              model.to(lowerCAmelCase    )
              model.eval()
              lowerCamelCase_				=model(
                  input_ids=lowerCAmelCase,						decoder_input_ids=lowerCAmelCase,						attention_mask=lowerCAmelCase,						decoder_attention_mask=lowerCAmelCase,						)
              lowerCamelCase_				=model(input_ids=lowerCAmelCase,						decoder_input_ids=lowerCAmelCase    )
              lowerCamelCase_				=result.last_hidden_state
              lowerCamelCase_				=result.past_key_values
              lowerCamelCase_				=result.encoder_last_hidden_state

              self.parent.assertEqual(encoder_output.size(),						(self.batch_size, self.encoder_seq_length, self.hidden_size)    )
              self.parent.assertEqual(decoder_output.size(),						(self.batch_size, self.decoder_seq_length, self.hidden_size)    )
              # There should be `num_layers` key value embeddings stored in decoder_past
              self.parent.assertEqual(len(lowerCAmelCase    ),						config.num_layers    )
              # There should be a self attn key, a self attn value, a cross attn key and a cross attn value stored in each decoder_past tuple
              self.parent.assertEqual(len(decoder_past[0]    ),						4    )






       def 	lowercase__	(				self,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						lowerCAmelCase,						):



              """simple docstring"""







              lowerCamelCase_				=UMTaModel(config=lowerCAmelCase    ).get_decoder().to(lowerCAmelCase    ).eval()
              # first forward pass
              lowerCamelCase_				=model(lowerCAmelCase,						use_cache=lowerCAmelCase    )
              lowerCamelCase_				=model(lowerCAmelCase    )
              lowerCamelCase_				=model(lowerCAmelCase,						use_cache=lowerCAmelCase    )

              self.parent.assertTrue(len(lowerCAmelCase    ) == len(lowerCAmelCase    )    )
              self.parent.assertTrue(len(lowerCAmelCase    ) == len(lowerCAmelCase    ) + 1    )

              lowerCamelCase_, lowerCamelCase_				=outputs.to_tuple()

              # create hypothetical next token and extent to next_input_ids
              lowerCamelCase_				=ids_tensor((self.batch_size, 1),						config.vocab_size    )

              # append to next input_ids and
              lowerCamelCase_				=torch.cat([input_ids, next_tokens],						dim=-1    )

              lowerCamelCase_				=model(lowerCAmelCase    )['''last_hidden_state''']
              lowerCamelCase_				=model(lowerCAmelCase,						past_key_values=lowerCAmelCase    )['''last_hidden_state''']

              # select random slice
              lowerCamelCase_				=ids_tensor((1,),						output_from_past.shape[-1]    ).item()
              lowerCamelCase_				=output_from_no_past[:, -1, random_slice_idx].detach()
              lowerCamelCase_				=output_from_past[:, 0, random_slice_idx].detach()

              # test that outputs are equal for slice
              self.parent.assertTrue(torch.allclose(lowerCAmelCase,						lowerCAmelCase,						atol=1e-3    )    )






       def 	lowercase__	(				self,						lowerCAmelCase,						lowerCAmelCase,						):



              """simple docstring"""







              lowerCamelCase_				=UMTaModel(config=lowerCAmelCase    ).to(lowerCAmelCase    ).half().eval()
              lowerCamelCase_				=model(**lowerCAmelCase    )['''last_hidden_state''']
              self.parent.assertFalse(torch.isnan(lowerCAmelCase    ).any().item()    )





@require_torch
class     __UpperCamelCase			(					lowerCamelCase__     ,   lowerCamelCase__     ,   lowerCamelCase__     ,   unittest.TestCase							):
       lowercase      :       Union[str, Any] 		=(
           (UMTaModel, UMTaForConditionalGeneration, UMTaForQuestionAnswering) if is_torch_available() else ()
       )
       lowercase      :       Optional[Any] 		=(UMTaForConditionalGeneration,) if is_torch_available() else ()
       lowercase      :       List[str] 		=(
           {
               '''conversational''': UMTaForConditionalGeneration,
               '''feature-extraction''': UMTaModel,
               '''summarization''': UMTaForConditionalGeneration,
               '''text2text-generation''': UMTaForConditionalGeneration,
               '''translation''': UMTaForConditionalGeneration,
               '''question-answering''': UMTaForQuestionAnswering,
           }
           if is_torch_available()
           else {}
       )
       lowercase      :       Union[str, Any] 		=True
       lowercase      :       List[Any] 		=False
       lowercase      :       str 		=False
       lowercase      :       Optional[Any] 		=True
       lowercase      :       str 		=True
       # The small UMT5 model needs higher percentages for CPU/MP tests
       lowercase      :       Any 		=[0.8, 0.9]






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=UMTaModelTester(self    )






       @unittest.skip('''Test has a segmentation fault on torch 1.8.0'''    )
       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=self.model_tester.prepare_config_and_inputs()
              lowerCamelCase_				=UMTaModel(config_and_inputs[0]    ).to(lowerCAmelCase    )
              with tempfile.TemporaryDirectory() as tmpdirname:
                     torch.onnx.export(
                         lowerCAmelCase,						(config_and_inputs[1], config_and_inputs[3], config_and_inputs[2]),						f'''{tmpdirname}/t5_test.onnx''',						export_params=lowerCAmelCase,						opset_version=9,						input_names=['''input_ids''', '''decoder_input_ids'''],						)






       @unittest.skipIf(torch_device == '''cpu''',						'''Cant do half precision'''    )
       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model_fpaa_forward(*lowerCAmelCase    )






       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=['''encoder_attentions''', '''decoder_attentions''', '''cross_attentions''']
              lowerCamelCase_				=self.model_tester.prepare_config_and_inputs()
              lowerCamelCase_				=config_and_inputs[0]
              lowerCamelCase_				=UMTaForConditionalGeneration(lowerCAmelCase    ).eval()
              model.to(lowerCAmelCase    )

              lowerCamelCase_				={
                  '''head_mask''': torch.zeros(config.num_layers,						config.num_heads,						device=lowerCAmelCase    ),
                  '''decoder_head_mask''': torch.zeros(config.num_decoder_layers,						config.num_heads,						device=lowerCAmelCase    ),
                  '''cross_attn_head_mask''': torch.zeros(config.num_decoder_layers,						config.num_heads,						device=lowerCAmelCase    ),
              }

              for attn_name, (name, mask) in zip(lowerCAmelCase,						head_masking.items()    ):
                     lowerCamelCase_				={name: mask}
                     # Explicitly pass decoder_head_mask as it is required from T5 model when head_mask specified
                     if name == "head_mask":
                            lowerCamelCase_				=torch.ones(
                                config.num_decoder_layers,						config.num_heads,						device=lowerCAmelCase    )

                     lowerCamelCase_				=model.generate(
                         config_and_inputs[1]['''input_ids'''],						num_beams=1,						max_length=3,						output_attentions=lowerCAmelCase,						return_dict_in_generate=lowerCAmelCase,						**lowerCAmelCase,						)
                     # We check the state of decoder_attentions and cross_attentions just from the last step
                     lowerCamelCase_				=out[attn_name] if attn_name == attention_names[0] else out[attn_name][-1]
                     self.assertEqual(sum([w.sum().item() for w in attn_weights]    ),						0.0    )






       @unittest.skip('''Does not work on the tiny model as we keep hitting edge cases.'''    )
       def 	lowercase__	(				self    ):



              """simple docstring"""







              pass





@require_torch
@require_sentencepiece
@require_tokenizers
class     __UpperCamelCase			(					unittest.TestCase							):






       @slow
       @unittest.skip(
           '''Unless we stop stripping left and right by default for all special tokens, the expected ids obtained here will not match the original ones. Wait for https://github.com/huggingface/transformers/pull/23909 to be merged'''    )
       def 	lowercase__	(				self    ):



              """simple docstring"""







              lowerCamelCase_				=UMTaForConditionalGeneration.from_pretrained('''google/umt5-small''',						return_dict=lowerCAmelCase    ).to(lowerCAmelCase    )
              lowerCamelCase_				=AutoTokenizer.from_pretrained('''google/umt5-small''',						use_fast=lowerCAmelCase,						legacy=lowerCAmelCase    )
              lowerCamelCase_				=[
                  '''Bonjour monsieur <extra_id_0> bien <extra_id_1>.''',
                  '''No se como puedo <extra_id_0>.''',
                  '''This is the reason why we <extra_id_0> them.''',
                  '''The <extra_id_0> walks in <extra_id_1>, seats''',
                  '''A <extra_id_0> walks into a bar and orders a <extra_id_1> with <extra_id_2> pinch of <extra_id_3>.''',
              ]
              lowerCamelCase_				=tokenizer(lowerCAmelCase,						return_tensors='''pt''',						padding=lowerCAmelCase    ).input_ids
              # fmt: off
              lowerCamelCase_				=torch.tensor(
                  [
                      [ 38_530, 210_703, 256_299, 1_410, 256_298, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   826, 321, 671, 25_922, 256_299, 274, 1, 0,0, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [  1_460, 339, 312, 19_014, 10_620, 758, 256_299, 2_355,274, 1, 0, 0, 0, 0, 0, 0,0, 0],
                      [   517, 256_299, 14_869, 281, 301, 256_298, 275, 119_983,1, 0, 0, 0, 0, 0, 0, 0,0, 0],
                      [   320, 256_299, 14_869, 281, 2_234, 289, 2_275, 333,61_391, 289, 256_298, 543, 256_297, 168_714, 329, 256_296,274, 1],
                  ]    )
              # fmt: on
              torch.testing.assert_allclose(lowerCAmelCase,						lowerCAmelCase    )

              lowerCamelCase_				=model.generate(input_ids.to(lowerCAmelCase    )    )
              lowerCamelCase_				=[
                  '''<pad><extra_id_0> et<extra_id_1> [eod] <extra_id_2><extra_id_55>.. [eod] 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 💐 <extra_id_56>ajšietosto<extra_id_56>lleux<extra_id_19><extra_id_6>ajšie</s>''',
                  '''<pad><extra_id_0>.<extra_id_1>.,<0x0A>...spech <0x0A><extra_id_20> <extra_id_21></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> are not going to be a part of the world. We are not going to be a part of<extra_id_1> and<extra_id_2><0x0A><extra_id_48>.<extra_id_48></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0> door<extra_id_1>, the door<extra_id_2> 피해[/</s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
                  '''<pad><extra_id_0>nyone who<extra_id_1> drink<extra_id_2> a<extra_id_3> alcohol<extra_id_4> A<extra_id_5> A. This<extra_id_6> I<extra_id_7><extra_id_52><extra_id_53></s><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad><pad>''',
              ]
              lowerCamelCase_				=tokenizer.batch_decode(lowerCAmelCase    )
              self.assertEqual(lowerCAmelCase,						lowerCAmelCase    )










'''simple docstring'''







# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position

lowerCAmelCase_     						=				'''2.13.1'''

import platform

import pyarrow
from packaging import version


if version.parse(platform.python_version()) < version.parse('''3.7'''):
    raise ImportWarning(
        '''To use `datasets`, Python>=3.7 is required, and the current version of Python doesn\'t match this condition.'''
    )

if version.parse(pyarrow.__version__).major < 8:
    raise ImportWarning(
        '''To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn\'t match this condition.\n'''
        '''If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`.'''
    )

del platform
del pyarrow
del version

from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
    get_dataset_config_info,
    get_dataset_config_names,
    get_dataset_infos,
    get_dataset_split_names,
    inspect_dataset,
    inspect_metric,
    list_datasets,
    list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
    NamedSplit,
    NamedSplitAll,
    Split,
    SplitBase,
    SplitDict,
    SplitGenerator,
    SplitInfo,
    SubSplitInfo,
    percent,
)
from .tasks import *
from .utils import *
from .utils import logging


# deprecated modules
from datasets import arrow_dataset as _arrow_dataset  # isort:skip
from datasets import utils as _utils  # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager  # isort:skip

lowerCAmelCase_     						=				concatenate_datasets
lowerCAmelCase_     						=				DownloadConfig
lowerCAmelCase_     						=				DownloadManager
lowerCAmelCase_     						=				DownloadMode
lowerCAmelCase_     						=				DownloadConfig
lowerCAmelCase_     						=				DownloadMode
lowerCAmelCase_     						=				DownloadManager

del _arrow_dataset, _utils, _deprecated_download_manager


